Coastal Swamp Oak Forest (CSOF), a supratidal wetland community dominated by Casuarina glauca, is a widely distributed coastal ecosystem along Australia's east coast. These wetland communities are highly valuable for providing ecosystem services, including carbon sequestration. Positioned within the supratidal zone of estuaries ��� and often abutting upper intertidal saltmarsh and/or mangrove ��� CSOF may be vulnerable to salinity intrusion and increased tidal inundation due to sea-level rise. To understand spatial patterns of vegetation composition and structure in CSOF, field-based (in-situ) and remote-sensing approaches were employed on the Minnamurra floodplain, New South Wales, Australia. In-situ vegetation surveys within 23 field plots located along seaward to landward transects revealed large variations in mean tree height (2.5���13.1 m) and tree densities (100���8700 trees/ha). Unmanned Aerial Vehicles with Structure from Motion (UAV-SfM), and airborne Light Detection and Ranging (LiDAR) approaches returned mean plot canopy height estimates ranging between 0.1 and 12.8 m. Comparison of vegetation metrics between remote sensors (UAV-SfM and LiDAR) demonstrated similar capacities (R2 values > 0.85) to capture CSOF vegetation height. Comparison of field and spatial metrics elucidated a moderate correlation between the datasets for maximum canopy height (R2 > 0.6) which can be partially explained by the different spatial scales of measurement among these approaches. Canopy height, Normalised Difference Vegetation Index (NDVI), and point density (i.e., vegetation density) estimates were each positively correlated with elevation above mean sea-level. This coincides with indications of plant stress and/or mortality at the seaward edge of CSOF, and in topographic depressions. These findings suggest physico-chemical gradients exert a strong control on CSOF vegetation structure and health, with implications for the current acceleration of sea-level rise. When combined, remote sensing and field-based datasets are useful to characterise and quantify CSOF structure and distribution and can therefore be employed in future assessments of this understudied ecosystem.